Day 12

For next time

Please fill out the MP3 mid-point survey so we can collect general information about the data sets and tools people are using, which will let us provide better support.
Reminder: Please complete Reflection and Teaming Survey 2

Ethics in text mining and analysis

Alignment and bias

Let’s examine a reflection write-up from a previous semester MP3 that has been adapted to serve as a prompt that allows us to build up numerous “ask-analyze-assess” alignment possibilities. We will do so today with an eye toward biases that could have played a role in data and algorithms used (or the people that generated them) by the systems that this project incorporated.

Work with one or two people near you as you read the reflection below and do exercises 1 through 5 related to alignment. Examining an already-completed MP3 can give you practice considering limitations and biases in complex systems before you reflect upon your own assignment. The exercise also enables instructors to introduce some considerations that might be less obvious.

What the creator was asking:
- For this project, I created a program that asks the user for a music artist, scrapes the lyrics of their songs, and performs a sentiment analysis on them.
Which types of data were analyzed:
- I used google and lyricsfreak.com as data sources. Once I parsed through the HTML and gathered the lyrics of each song, I used a sentiment analyzer to gauge the overall mood of their songs. I hoped to get more comfortable using python to interact with information on the web and use an appropriate computational analysis on it. Additionally, I wanted to create a visual representation of artists’ lyrics to compare and contrast in hopes to find trends. Once the user enters an artist’s name, my program performs a google search with those terms and finds the artist’s song list on the LyricsFreak website. I looked at different lyric websites but I chose this one because the body of the lyrics was easy to identify and easily accessible using Beautiful Soup. From the artist’s page, I extracted the URLs of each song and went to each individual page to scrape the lyrics. Keeping the lyrics from each songs separate I used the NLTK sentiment intensity analyzer to find the polarity scores of each song. Finally, using matplotlib, I plotted each song by the selected artist on a scatter plot using the negative and positive scores from the analysis.
How the tools selected helped answer the question:
- Graphs for artists with a lot of songs were difficult to read. I tried for a long time to figure out how to get the labels to appear when they were hovered over, but I was unsuccessful and could not find a way to interact with the figures I generated. Instead, I randomly labeled a few of the songs so that the viewer could identify specific songs to make sense of the plot but still be able to observe any trends. The final representation of my information is a scatter plot with every song’s positive and negative sentiment values of a particular artist. One thing that surprised me was that the songs turned out to be a lot more neutral than I had expected. I tend to think songs written by an artist have the similar tones but judging by the analysis I performed, I don’t think I can fully support that hypothesis. However, there were slight trends, for example Adele’s songs were more negative than positive for the most part but generally the points were pretty scattered. Also, I thought it was interesting to look at the songs that were very close to either axis. This suggests that they were almost entirely negative or positive and more skewed to one side or the other.
- How the student felt that the questions they asked aligned with the data sources they chose and the tools they used to analyze them and present their work:
  - [Do the exercises below before viewing the author’s remarks in this section.]
- How the creator felt after completing Mini-Project 3:
  - After doing the project, the I am very pleased with the skill set I learned from this project. This was one of the first projects that allowed for some freedom in what topics and concepts we wanted to dive deeper into. I was definitely challenged by this project. One of the biggest changes I can pinpoint was visualizing the flow of the program and going from what was in my head to lines of code. I had to make decisions about which functions would handle individual song lyrics and which ones would be performing a function on a list of scrapped information. It helped me to write out the different functions I thought I needed in plain english along with the parameters and return objects and to create a flow diagram. I felt as though this was well scoped and that I gained a significant amount of independence during this project. Although I might not use the specific results from this project, I now have a handful of new tools that I look forward to incorporating in my future projects.
- Exercises: put youselves in this students’ shoes and consider the tools mentioned above or any others that you can imagine helping to achieve the stated goal.
1. Take the potential source of lyrics, lyricsfreak.com and Google search results, and propose at least one way that each source may be limited.
  - 1a. A potential lyricsfreak.com limitation…
  - 1b. A potential google search limitation…
2. Take the tool to analyze the language of lyrics, NLTK, and imagine ways that the tool might have been trained to classify a set of words as positive or negative. Think of times when such a toolkit may struggle to classify a particular set of lyrics based on how you think it was trained.
  - 2a. NLTK might produce satisfactory results given lyrical data that has the following characteristics, based on how we think it was trained…
  - 2b. NLTK might struggle to produce satisfactory results given lyrical data that has the following characteristics, based on how we think it was trained…
3. Consider the tool for visualizing the results of the analyses, matplotlib, or something similar (e.g., printing text to the terminal). What are conditions (e.g., with a lot of lyrical data available for an artist or only a little) that might might make it difficult for a user to confirm or challenge assumptions they made about the musician they searched for?
4. Once you have responses to the three prompts above, read the alignment excerpt written by the original project creator.
5. Contemplate whether you went further in your consideration of potential bias than the project’s creator. [Optional] see texts such as Algorithms of Oppression that introduce ways in which Google searches and the algorithms that produce the results can be problematic for many users.

Text caching class example

We’ve created an example program to demonstrate a) caching text data as local files, and b) the utility of custom classes.

You are free to build on this program in your MP3, but as with all code you didn’t write you must make sure you understand how it works (and ask questions if you don’t). Since this code was provided by course staff, you don’t need to cite its source.

Exercise: Try adding a lines method to the class that returns a list of all the individual lines in the text file, so that you can use it to write code like:

example = Text(my_url)
for line in example.lines():
    do_something(line)